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Analytical prior information can cut prediction errors by over 89% compared to direct learning methods when simulation data is limited.
The Sinkhorn linearization reveals that the estimator's convergence properties hinge on a delicate balance of spectral characteristics, redefining our approach to inverse optimal transport.
Rule-Aware FinBERT boosts financial sentiment classification accuracy by over 6% while adding only 1,024 trainable weights, showcasing the power of integrating rule-derived features with contextual embeddings.
